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Testbed Roundup: The GOES-R Proving Ground

Steve Goodman

GOES-R Series Chief Scientist

http://www.goes-r.gov

4th NOAA Testbed and Operational

Proving Ground Workshop

College Park, MD, April 2-4, 2013

Contributors

James Gurka NOAA/NESDIS/GOES-R Program Scientist

Timothy Schmit, Mark DeMaria

NOAA/NESDIS/Center for Satellite Applications and Research (STAR)

Anthony Mostek and Brian Motta NWS Training Division, Boulder CO

Chris Siewert, Kristin Calhoun

NCEP /SPC / CIMMS, Norman, OK

Michael Folmer NCEP/ HPC/ OPC / CICS, College Park, MD

Amanda Terborg

NCEP/ AWC / CIMSS, Kansas City MO

Chad Gravelle NWS/OPG / CIMSS, Kansas City MO

Kathryn Mozer

ASRC Federal Space and Defense

The GOES-R Proving Ground engages NWS in pre-operational demonstrations of selected capabilities of next generation GOES

• Objective is to bridge the gap between research and operations by:

– Utilizing current systems (satellite, terrestrial, or model/synthetic) to emulate

future GOES-R capabilities

– Infusing GOES-R products and techniques into NWS operations with emphasis on AWIPS and transitioning to AWIPS-II.

– Engaging in a dialogue to provide feedback to developers from users

• The Proving Ground accomplishes its mission through:

– Sustained interaction between developers and end users for training,

product evaluation, and solicitation of user feedback.

– Close coordination with GOES-R Algorithm Working Group (AWG) and Risk Reduction programs as sources of demonstration products, promoting a smooth transition to operations

Intended outcomes are Day-1 readiness and maximum utilization for both the

developers and users of GOES-R products, and an effective transition to operations

GOES-R Proving Ground

GOES-R Products

Advanced Baseline Imager (ABI)

Aerosol Detection (Including Smoke and Dust)

Aerosol Optical Depth (AOD)

Clear Sky Masks

Cloud and Moisture Imagery

Cloud Optical Depth

Cloud Particle Size Distribution

Cloud Top Height

Cloud Top Phase

Cloud Top Pressure

Cloud Top Temperature

Derived Motion Winds

Derived Stability Indices

Downward Shortwave Radiation: Surface

Fire/Hot Spot Characterization

Hurricane Intensity Estimation

Land Surface Temperature (Skin)

Legacy Vertical Moisture Profile

Legacy Vertical Temperature Profile

Radiances

Rainfall Rate/QPE

Reflected Shortwave Radiation: TOA

Sea Surface Temperature (Skin)

Snow Cover

Total Precipitable Water

Volcanic Ash: Detection and Height

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Geostationary Lightning Mapper (GLM)

Lightning Detection: Events, Groups & Flashes

Space Environment In-Situ Suite (SEISS)

Energetic Heavy Ions

Magnetospheric Electrons & Protons: Low Energy

Magnetospheric Electrons: Med & High Energy

Magnetospheric Protons: Med & High Energy

Solar and Galactic Protons

Magnetometer (MAG)

Geomagnetic Field

Extreme Ultraviolet and X-ray Irradiance Suite (EXIS)

Solar Flux: EUV Solar Flux: X-ray Irradiance

Solar Ultraviolet Imager (SUVI)

Solar EUV Imagery

Baseline Products

Advanced Baseline Imager (ABI)

Absorbed Shortwave Radiation: Surface Aerosol Particle Size Aircraft Icing Threat Cloud Ice Water Path Cloud Layers/Heights Cloud Liquid Water Cloud Type Convective Initiation Currents Currents: Offshore Downward Longwave Radiation: Surface Enhanced “V”/Overshooting Top Detection Flood/Standing Water Ice Cover Low Cloud and Fog Ozone Total Probability of Rainfall Rainfall Potential Sea and Lake Ice: Age Sea and Lake Ice: Concentration Sea and Lake Ice: Motion Snow Depth (Over Plains) SO2 Detection Surface Albedo Surface Emissivity Tropopause Folding Turbulence Prediction Upward Longwave Radiation: Surface Upward Longwave Radiation: TOA Vegetation Fraction: Green Vegetation Index Visibility

Future Capabilities

– Hazardous Weather Testbed

• Focus on Severe Storms – NHC/Joint Hurricane Testbed

• Focus on tropical cyclones/hurricane intensity and track – Aviation Weather Testbed

• Focus on High Impact Convective Weather – OPC and SAB (Camp Springs MD)

• Focus on offshore thunderstorms – High Latitude and Arctic Experiment (Alaska Region)

• Focus on precipitation/snow/cloud/ash/aviation – HPC and SAB (Camp Springs MD)

• Focus on precipitation/QPF – Air Quality (UMBC)

• Focus on aerosol detection – Pacific Region (Hawaii)

• Focus on tropical cyclones/heavy rainfall/aviation – Space Weather (NWS SWPC: Boulder CO)

• Focus on GOES-R like level 2 products

2012 Demonstrations

GOES-R Proving Ground Partners

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3-7

GOES-R Proving Ground Reports

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FY13 R3 Funding by Topic

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0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

FY13 Funds $

FY13 Funds

NOAA PMM

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NWS Operational Advisory Team (NOAT) Yearly Guidance Memorandum for the Science and

Demonstration Executive Board (SDEB) – FY13

• Overarching NWS Science and Technology Themes

– Convective initiation/Warn on Forecast

– Best state of the Atmosphere (e.g., 3-d analysis)

– Next Generation Forecast System

– Decision Support Information Systems

– Integration of Social Science into the forecast process

– Risk Reduction as a core validation activity

• NWS Weather Ready Nation (WRN)

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*NOAT Priorities for GOES-R Future Capabilities

• 1. Convective Initiation

• 2. Fog and Low Stratus

• 3. Icing Threat plus Cloud Properties (cloud ice water path, cloud layers heights, cloud liquid water, cloud type). Note: these are all interrelated – cloud properties integral to this and other efforts. Also, specific guidance to pursue integrated NWP-centric approaches.

• 4. SO2 Detection

• 5. Land Surface Model Related (emissivity, vegetation index, vegetation fraction)

• 6. Precipitation: probability of rainfall, rainfall potential, QPE (Rain Rate)

• 7. Ice Cover

• 8. Flood and Standing Water (at full resolution)

• 9. Other Priority 2 Products not specifically noted (includes tropopause folding turbulence prediction, enhanced V overshooting top detection, visibility, and all others not covered above).

Although demonstration of products should meet these priorities, NOAT accepts the demonstration of non-baseline products as acceptable if short-term value to operations is expected.

*NOAT- NWS Ops Advisory Team

FY13 Risk Reduction New Starts

GOES R3 Topic Project/PI

Aviation Satellite-Detected OT/TOT CIMSS/Velden, LaRC/Bedka

Infrastructure AWIPS-II Satellite Plug-ins (EPDT) NASA/Jedlovec, STAR-CIRA/DeMaria, STAR-CIMSS/Schmit

Clouds Fog and Low Stratus Detection STAR/Pavolonis

Lightning GLM Lightning Jump Algorithm Pre-Operational National Demonstration UAH/Carey, OU-CIMMS-NSSL/Calhoun

Severe Weather Convective Initiation (GOES-R Fused NWP) UAH/Mecikalski, GSD/Benjamin, STAR/Heidinger

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*IAC Recommendation Science Week March 2013

… Utilization of the LEO high spectral resolution data rendering of moisture vertical and horizontal distributions needs to be encouraged.

Recommendation: Regional forecasts and nowcasts necessary for a Weather Ready Nation will have to make better use of the information content from AIRS, CrIS, and IASI data; GPS data should also be included. Between LEO sounding coverage, GOES-R data should be used to monitor temporal profile changes (atmospheric stability, d𝑞/dt, V • 𝛻𝑞, 𝛻 • 𝑞 , etc).

*IAC- GOES-R Independent Advisory Committee

• Visiting Scientists- High Impact Weather NWP

• Haidao Lin- ESRL/CIRA

• Thomas Jones-NSSL/OU-CIMMS

• Xiaoyan Zhang- EMC/CICS

• JCSDA Computing Infrastructure Enhancements

— S4 supercomputer at UW-CIMSS

— Jibb supercomputer at NASA GMAO

— Governance and User accounts in place

— NCEP Global Forecast system installed to advance R2O

• Meetings-Workshops

— Warn on Forecast-High Impact Weather (Norman, OK, February 5-7)

— NOAA Satellite Science Week (Virtual, March 18-22)

— NOAA Testbed-Proving Ground Workshop (College Park, MD, April 2-4, 2013)-focus on High Impact Events

— NOAA Satellite Conference (College Park, MD, April 8-12)- special session on NWP and Data Assimilation for TCs, hurricanes, and heavy precipitation

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GOES-R NWP Program Updates

SPD Director Greg Mandt and Program Chief

Scientist Steve Goodman tour the Super

Computer for Satellite Simulation and Data

Assimilation Studies (S4) at UW-CIMSS.

The Joint Center in a big box (Jibb)

supercomputer at NASA GMAO

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GOES 14 SRSOR Experiment http://cimss.ssec.wisc.edu/goes/srsor/GOES-14_SRSOR.html

• Outcomes: Operational use of ABI and GLM for nowcasting

• 1 min imagery, 1 minute lightning, 1 min radar volume scans

• Time: August 16-October 31, 2012

• Locations:

• Norman, OK- NEXRAD, MPAR, OKLMA (primary site)

• Huntsville, AL- NEXRAD, UAH dual-pol radars, NALMA

• Sterling, VA- NEXRAD, TDWR, DCLMA

• Fort Collins, Colorado- NEXRAD, CSU-CHILL, NCLMA

• Melbourne/KSC, FL- NEXRAD, LDAR II

• Atlantic Ocean/GulfMex Basin- NASA EV-1 Hurricane and Severe

Storm Sentinel-HS3 science flights 20 Sept-5 Oct coincidence with

GOES-R products (http://espo.nasa.gov/missions/hs3) 14

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The GOES-R Algorithm Work Group in partnership with the GOES-R Risk

Reduction Science Program and Proving Ground Demonstration Program

have developed a number of products and decision aids undergoing evaluation

and feedback with NWS forecasters across the country.

In the GOES-R Proving Ground, Baseline and Future Capability Products are

demonstrated with and receive feedback from forecasters using proxy and

simulated data sets. Some of the key products that are very useful for high impact

weather forecasts and warnings include:

• Cloud and Moisture Imagery

• Hurricane Intensity Estimate

• Convective Initiation

• Overshooting Top Detection

• Lightning Detection

A deficiency in these product demonstrations is our inability to more fully

demonstrate the added utility of the GOES-R imagery products at the higher 30

sec to 1 min mesoscale refresh rate that will be routinely possible with the ABI.

GOES-14 SRSOR 1-min Imagery Experiment http://cimss.ssec.wisc.edu/goes/srsor/GOES-14_SRSOR.html

GOES-14 SRSOR Experiment : Overshooting Top Detection

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Nor’easter on 11/07/2012 GOES-13 Infrared overlaid with Overshooting Tops

HWT Spring Experiment

– Forecasters pleased with “Strength of Signal” output from

SATCAST CI • Used SATCAST 83% of time during warning ops in EWP • Increased SATCAST strength 30-60 min prior to CI with

potential for longer lead times for severe • “We’ve seen this all week… UAH gives a heads up that general

convection is building, then a short time later UW-CTC typically picks up on the stronger convection building.”

• Increase in false alarm rate over high terrain • 88% of participants comfortable using the product

– Cloud Top Cooling Rate (UW-CTC) • Used CTC 89% of time during warning ops • Lead time over occurrence of 60 dBZ composite reflectivity and

1.0 MESH: 10 – 90 min with most around 30 min • With knowledge of environment and ongoing supercell

activity… warnings issued with CTC

HWT Spring Experiment – Simulated Satellite Imagery

• “Synthetic WRF imagery can enhance forecasts by providing model data in a familiar satellite format which makes model analysis, model comparison to obs and model forecast projections easier to visualize and understand”

– Psedo Geostationary Lightning Mapper (pGLM) • Total lightning data showed good correlation with updraft

intensity and typically seen “well ahead of the first CG (cloud to ground) flash

• Pulled focus to individual storms of interest • Particularly useful during days with marginally severe and

numerous storms over CWA

– Nearcast • Forecasters used Nearcast product 70% of time in warning ops • Found instability fields particularly useful in determining

convective maintenance

– GOES Sounder Airmass RGB • Useful for quick look at synoptic scale atmosphere • “Looping this product can show airmass movement and jet

position/structure”

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NWS Vision to Integrate ABI and GLM Products with Other Data and Models

A Potential Operational Example: Convective Initiation/Severe Wx

How can we integrate the information in future tools?

Why NWS needs this?

Situational Awareness

Warning confidence

Decision Support (venues)

CI

Over-

shooting

tops

Lightning

Jumps

Next Generation

Warning System

Situational Awareness:

User comment: ‘Cloud Top Cooling product is an excellent source of enhancing the situational awareness for future convective initiation, particularly in rapid scan mode’.

AWC Testbed forecaster

(June 2012)

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West Gulf RFC Data Gaps

Multi-sensor Mosaic Multi-sensor Mosaic and Satellite Estimates

Satellite QPE is used where there is no dependable radar estimates or

rain gauge data

Greg Story

NWS/WGRFC

WGRFC is responsible for deriving basin averaged areal precipitation for every location inside the red outline-

this is outside the range of the WSR-88D, and there are roughly 15 real-time rain gauges over this region.

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GOES-R Rain Rate Algorithm- SCaMPR Self-Calibrating Multivariate Precipitation Retrieval

• The GOES-R Rainfall Rate algorithm (developed from SEVERI proxy data) estimates instantaneous rain rate every 15 min on the ABI full disk at the IR pixel resolution (~ 2 km) with a latency of less than 5 min from image time.

• Primary focus is operational flash flood forecast support

• The rain rates will be derived from the ABI IR bands, calibrated against rain rates from MW instruments.

• This will allow the rapid refresh and high spatial resolution of IR data from GEO while attempting to capture the accuracy of MW rain rates from LEO.

• A 2-channel version of this algorithm modified for current GOES has been running in real time since August 2011 in support of GOES-R Proving Ground

• Current GOES Imager does not have the 6.2, 8.5, and 12.0 µm bands

T6.19 T6.7 T8.5 - T7.34

S = 0.568-(Tmin,11.2-217 K) T11.2 - T7.34T11.2-T6.7

Tavg,11.2 - Tmin,11.2 - S T8.5 - T11.2

T7.34 - T6.19 T11.2 - T12.3

GOES-R Precipitation Projects: FY11-13 GOES-R Risk Reduction

PI Project

Bob Adler CICS

Combining GLM and ABI Data for Enhanced GOES-R Rainfall Estimates

Xiquan Dong UND

Improving GOES-R Cloud and Precipitation Products Associated with Deep Convective Systems by using NEXRAD Radar Network over the Continental U.S

Bob Rabin NSSL

Improvements to QPE using GOES visible ABI and model data

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IR Tb

CST +

Lightning

(Conv/Strat)

CST

(Conv/Strat)

PMW

(Conv/Strat

10 mm/hr)

CST Comparisons Adler et al.- CST confused by thick cirrus, thick anvil debris, or large MCS cloud shield. Lightning info.

(2002-2008) consistently improved the convective detection (POD) by 8%, lowers the false alarm (FAR)

by 30%.

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CSTL RR - SCaMPR Comparison P

MW

Rad

ar

SC

aM

PR

CS

TL

(Fin

al P

rod

uct)

GOES-R Precipitation Projects: New Starts for NOAA Members of the

NASA PMM (GPM) Science Team

PI Project

Pingping Xie NWS/CPC

Development of CMORPH and GPM Day-1 Level 3 Precipitation Products for Improved Weather, Climate, and Water Applications

Jonathan Gourley NSSL

WiMerge: Research and Development of Unified CONUS 3-D Mosaics and QPE products

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Atmos River

Topography ORI

VIS WRF

IR

Blended TPW

> 50 mm

TPW Anomaly

> 200%normal

Nashville flood

GOES-R Clouds, Moisture

Bob Rabin Sheldon

Kusselson

Dan Bikos

Stan Kidder

NHC Highlights

• Many SRSO cases obtained from GOES-14 – ASPB and CIMSS lead

• Most RGB and related products in N-AWIPS – Air Mass (SEVIRI and GOES sounder), Dust, Pseudo-

Natural Color, SAL

• Very positive feedback on RGB Air Mass Product – Used in NHC forecast discussions and TAFB tropical

weather discussions

• NHC leveraging data distribution from SPoRT to get routine microwave imagery – One of the most useful NHC PG contributions so far

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Hurricane Sandy (2012) GOES-Sounder RGB Air Mass overlaid with WPC surface analysis

Genesis of Hurricane Sandy GOES-13 Infrared overlaid with 30-minute GLD-360 CG strikes

Atmospheric Motion Vectors from GOES-R Proxy: AMVs from special GOES-14, 1-min super-rapid-scan operations

Hurricane Sandy

AMVs from 15-min images (routine GOES sampling) AMVs from 1-min images (meso GOES-R sampling)

C. Velden (CIMSS) 1800 UTC 26 Oct, 2012

Low-Level (700-950 hPa) Vectors from VIS

AWC GOES-R Proving Ground

GOES-R Product Operational Status

Fog and Low Stratus* AWC ops – July 2012

Cloud Top Cooling# AWC ops – Oct. 2012

Overshooting Top/Enhanced “V” Detection# AWC ops/testbed – Oct.

2012

Simulated Satellite Imagery (NSSL-WRF and NAM Nest)*# Testbed – ops 2013

Pseudo Geostationary Lightning Mapper# Testbed – ops 2013

NearCasting Model# Testbed

ACHA Cloud Height Algorithms*# Testbed

Flight Icing Threat* Testbed

GOES-R Convective Initiation (UAH)# Testbed

RGB Airmass NAM ops – Oct. 2012

*Winter Experiment: 11-22 February 2013 #Summer Experiment: 12-23 August 2013

AWC 2012 Highlights

• CTC: ‘this product is an excellent source of enhancing the situational awareness for future convective initiation, particularly in rapid scan mode’

• NearCasting: ‘The product was VERY useful in terms of assessing where the atmosphere would be most favorable for convection should there be a trigger and/or broad-scale lift support’

• Fog/Low Cloud: ‘This product will be very helpful in identifying both advancing and dissipating fog layers, particularly on the West Coast.’

• PGLM: This product showed potential in identifying electrically active areas not associated with CG strikes.

• FIT showed higher icing probabilities over FL and the GulfMex

• FIT was able to pick out areas with a higher likelihood of icing, corroborated by the icing PIREPs in the same region

• Though only over a small portion of the Gulf of Mexico, the product has the potential to be useful over data sparse areas, such as large bodies of water, where observations and model data may not be available.

2013 Winter Experiment Flight Icing Threat

2012 Summer Experiment Pseudo Geostationary Lightning Mapper and GOES-R Convective Initiation Toolbox

• The GLM will be of particular use in the tropics, as much convection in those areas

contains intra-cloud lightning as well as CG

• The ABI OTD and GLM will also be of use in data sparse areas, such as over large

bodies of water and regions with limited radar coverage

Training and Education

Online Training Modules • GOES-R: Benefits of Next-Generation

Environmental Monitoring (COMET)

• GOES-R 101

• Satellite Hydrology and Meteorology for Forecasters (SHyMet)

• SPoRT product training modules

• Commerce Learning Center

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Outreach Projects (with NWSFOs) • COMET will reach out to the GOES-R Proving

Ground Partners and connect them with university faculty to use current and prototype data products for the purpose of building a bridge from products that are currently available to those that will become available when GOES-R is launched.

Printed Materials • GOES-R Fact Sheets (17)

• GOES-R Tri-fold

FY11-12 Live Training Sessions Synthetic Imagery in Forecasting Orographic Cirrus (January 2011) Synthetic Imagery in Forecasting Severe Weather (February 2011) Objective Satellite-Based Overshooting Top and Enhanced-V Anvil Thermal Couplet Signature Detection (February 2011) Volcanoes and Volcanic Ash Part 2 (March 2011) GOES-15 Becomes GOES-West (December 2011) VISIT Satellite Chats (short, interactive discussions, Q&A, monthly since February 2012) Topics: Fog and Low-Cloud Detection from Satellite (2-22-2012) Water Vapor Imagery (3-21-2012) Satellite Related Severe Weather Products (4-25-2012) Fire Weather Imagery and Products (5-23-2012) Mesoscale Convective Vortices (6-27-2012) Synthetic Imagery in Forecasting Low Clouds and Fog (April 2012) Pseudo GOES Lightning Mapper (May 2012) Tropical Cyclone Intensity Model Guidance Used by NHC (June 2012, updated) Tropical Cyclone Track Model Guidance Used by NHC (June 2012, updated) Convective Cloud Top Cooling, UW Convective Initiation Algorithm (July 2012)

Virtual Institute for Satellite Integration Training

Future Plans: 2013 And Beyond

• Continue to apply lessons learned to incorporate new improvements each year. Example:

• From HWT Spring Experiment… obvious that forecaster application of new products improves with additional training. In 2012 forecasters had access to satellite training material prior to arrival in Norman.

• Demonstrate products and decision aids in NOAA Testbeds, NCEP Centers, WFOs, and the NWS Proving Ground at Training Center

• Transition from Warning Related Products to remaining Baseline Products, Day 2 Future Capability, fused products, Decision Aids, Decision Support Services

• Continue to develop, demonstrate, and test as part of decision support services

• Enhanced JPSS, international, and broadcaster community collaboration

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Pre-launch demonstrations with proxy data benefits users to prepare them to fully exploit all GOES-R instruments and capabilities

Summary

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• Continue to apply lessons learned to incorporate new improvements each year.

• Demonstrate products and decision aids in NOAA Testbeds, NCEP Centers, WFOs, and the NWS Proving Ground at Training Center

• Transition of Water Cycle/Hydrology Future Capabilities, fused products, Impact-based Decision Aids, Decision Support Services

• Continue to develop, demonstrate, and test as part of decision support services

• Enhanced collaboration with JPSS, international, and private sector community